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Design and Implementation of a Graphic Simulator for Calculating the Inverse Kinematics of a Redundant Planar Manipulator Robot

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Design and Implementation of a Graphic Simulator for Calculating the Inverse Kinematics of a Redundant Planar Manipulator Robot applied sciences Article Design and Implementation of a Graphic Simulator for Calculating the Inverse Kinematics of a Redundant Planar Manipulator Robot Claudio Urrea * and Daniel Saa Department of Electrical Engineering, Universidad de Santiago de Chile, Av. Ecuador 3519, Estación Central, Santiago 9170124, Chile; [email protected] * Correspondence: [email protected]; Tel.: +56-2-27180324 Received: 2 August 2020; Accepted: 24 September 2020; Published: 27 September 2020 Abstract: In this paper, a graphics simulator that allows for characterizing the kinematic and dynamic behavior of redundant planar manipulator robots is presented. This graphics simulator is implemented using the Solidworks software and the SimMechanics Toolbox of MATLAB/Simulink. To calculate the inverse kinematics of this type of robot, an algorithm based on the probabilistic method called Simulated Annealing is proposed. By means of this method, it is possible to obtain, among many possibilities, the best solution for inverse kinematics. Without losing generality, the performance of metaheuristic algorithm is tested in a 6-DoF (Degrees of Freedom) virtual robot. The Cartesian coordinates (x,y) of the end effector of the robot under study can be accessed through a graphic interface, thereby automatically calculating its inverse kinematics, and yielding the solution set with the position adopted by each joint for each coordinate entered. Dynamic equations are obtained from the Lagrange–Euler formulation. To generate the joint trajectories, an interpolation method with a third order polynomial is used. The effectiveness of the developed methodologies is verified through computational simulations of a virtual robot. Keywords: simulation; redundant manipulator robots; kinematics; dynamics; simulated annealing; solidworks 1. Introduction Nowadays, the use of manipulator robots is widely extended in the industry because these robots carry out tasks that pose risks to humans, improve the quality of the products manufactured, and increase productivity. Nevertheless, robotics is not limited to the industry, but has also expanded to other fields such as medicine (robotic implants or remote surgical intervention systems), agriculture (sheep shearing or pork cutting), space (planetary surface exploration), and geological and military research, among others [1–3]. Manipulator robots can be classified into serial and parallel based on their structure. The links of a serial manipulator are normally assembled together by means of rotational or prismatic joints that form an open kinematic chain whose structure is similar to a human arm. This configuration allows for assembling a clamp or any other tools that can be moved to any position within the robot’s workspace [4]. The industrial robots available in the global markets are not redundant, but they do have the degrees of freedom necessary for performing the tasks for which they were designed [5]. However, if a failure occurs in the actuator of an industrial robot, without a safety backup, its respective joint blocks, and the robot losses the mobility of that joint. This may lead to situations that imply risks to the operators, difficulties for the users, interruption of the mining or industrial process, and economic losses, among other consequences [6]. Therefore, interest has arisen in the study of the behavior of Appl. Sci. 2020, 10, 6770; doi:10.3390/app10196770 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 6770 2 of 18 redundant manipulator robots, owing to a design that gives them more degrees of freedom than necessary for performing a specific task [7]. In general, the kinematic and dynamic study of robotic systems can be conducted in a didactic way, using graphic simulators. The kinematic problem can be solved through the Geometric Method, but, in practice, this method is not widely used because of the complexity of applying it to redundant manipulator robots, which has led to the development of several alternative methods. Kinematics is divided into direct and inverse kinematics (IK) that can be derived based on the standard Denavit-Hartenberg (D-H) model, which describes the geometry of the robot. Besides the almost universally used the D-H parameters [8–11], other methods such Algebra and Lie Groups are also useful to obtain the kinematics of the system [12]. The inverse kinematics chain of redundant manipulator robots is much more difficult to obtain. Its solution is fundamental to deal with problems such as route planning, visually guided movement, grabbing objects, etc. [13]. Currently, the conventional methods for solving inverse kinematics are divided into two categories: analytical (geometric-algebraic) and numerical (iterative algorithms) [14]. Some recent articles show that analytical solutions have only been found for some specific structures of redundant manipulator robots. For instance, Qian et al. [15] present a close loop intuitive solution for a space manipulator robot with 7-DoF and a PYR-P-PYR (Pitch-Yaw-Roll) structured based on the blockage of the first joint P. Teja and Shah [16] present the Inverse Coordinates Method for a special S-RRR (Spherical-Rotary) configuration, which is based on the inversion of the base and the end effector of the manipulator robot. Additionally, Zhao et al. [17] employ analytical, geometric and algebraic methods for a 6R industrial robot, which are combined with the Paden–Kahan subproblem and the matrix theory. Furthermore, since there are no possible analytical solutions for the inverse kinematics of some robots, some methods are now under discussion, for example, the Iterative Method, which is based on numerical methods [18]. Huang and Nie [19] present a numerical solution, which is achieved through the application of the Sequential Recovery Method to a robot with and RPY (Roll-Pitch-Yaw) configuration based on the modification of the Newton-Raphson Method. Another alternative to obtain the inverse kinematics is using the JT transpose or the J−1 inverse of the Jacobian matrix [20]. The usefulness of the JT Jacobian is demonstrated by Besset and Taylor [21]. In addition, Farzan and DeSouza [22] show a solution based on estimates of the inverse Jacobian matrix. Recently, as some traditional methods have become obsolete for some manipulator robots, researchers have started the development or artificial intelligence techniques for calculating inverse kinematics. Among these techniques or meta-heuristic algorithms, artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE) and simulated annealing (SA) stand out. Köker and Cakar [23] propose a hybrid smart solution for IK, which includes ANN, GA and SA and is applied to a 4-DoF robot. The weak point of this study is that optimization processes based on SA have a prolonged execution time due to the difficulties to select suitable cooling parameters. López et al. [24] present different meta-heuristic algorithms to solve the inverse kinematics of a KUKA Youbot mobile robot with 9-DoF. From simulations and real experimental results, it is demonstrated the superiority of the DE algorithm over other algorithms due to its computational cost, reduced joint movement and minimum error between the end effector and the target point. Additionally, López et al. [24] demonstrate that the hybrid biogeography-based optimization (HBBO) approach generates good results but with high computational costs. In contrast, cuckoo search (CS) and PSO report poor results and are not suggested as methods for obtaining inverse kinematics. The flaw of this study regarding the DE algorithm, is the number of iterations used, which slows down the search for a solution. Dereli and Köker [25] compare the position error and calculation time of two heuristic techniques called PSO and ABC. The simulations show that both algorithms successfully find the angles of the joints of a redundant manipulator robot with 7-DoF. Despite its good performance, the long steps of the search process and the excess of parameters are considered the disadvantages of this method. The algorithms above suffer a common problem: the time Appl. Sci. 2020, 10, 6770 3 of 18 consumed during the iteration process. In this regard, Zhou et al. [26] propose a smart algorithm based on extreme machine learning (ELM) and sequential mutation genetic algorithm (SGA) to improve the efficiency of the Standford MT-ARM manipulator robot of 6-DoF. Nevertheless, this last study sacrificed the accuracy of the preliminary solution of inverse kinematics. Currently, several researchers have implemented simulation systems them in robots [24,26,27] because they allow for manipulating and testing the control system of automated robot within a virtual environment. For example, Kereluk and Emani [28] present an integrated design and simulation environment that offers control over the hardware of a reconfigurable manipulator robot with 18-DoF denominated MARS. In this environment, the user can select or design the configuration of the manipulator robot, simulate its operation in a virtual reality environment and operate the physical manipulator robot. The simulation results have proved to be very similar to experimental results. López et al. [24] developed a simulator on MATLAB software to test the performance of different meta-heuristic algorithms for solving the inverse kinematics of manipulator robots. Zhou et al. [26] also used it to analyze, specifically, time efficiency during the training stage of NNs and ELM, as well
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